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index.html

@@ -185,29 +185,32 @@
                 <span class="model-name">FlameF0X/MathGPT2</span>
                 <span class="model-params">81.9M parameters</span>
             </div>
-            <p>The highest performer among tested models, demonstrating remarkable mathematical abilities despite its tiny parameter count. Shows particular strength in addition operations with 63.1% accuracy.</p>
+            <p>The highest performer among tested models, demonstrating remarkable mathematical abilities despite its tiny parameter count. Shows particular strength in addition operations with 58.3% accuracy and subtraction with 57.1% accuracy.</p>
             <div class="performance-highlight">
-                <strong>Overall math accuracy:</strong> 42.7% on 1000 test questions
+                <strong>Overall math accuracy:</strong> 42.0% on 100 test questions
             </div>
         </div>
         
         <div class="model-card">
             <div class="model-info">
-                <span class="model-name">PingVortex/VLM-1</span>
-                <span class="model-params">124M parameters</span>
+                <span class="model-name">aquif-moe-800m</span>
+                <span class="model-params">800M parameters</span>
             </div>
-            <p>The second best performer, scoring 4.8% overall accuracy. Shows more balanced performance across operations with particular strength in division (14.2%).</p>
+            <p>The second best performer, scoring 39.0% overall accuracy. Shows exceptional performance in subtraction (76.2%) and solid performance in addition (54.5%).</p>
             <div class="performance-highlight">
-                <strong>Operation strength:</strong> 14.2% accuracy on division
+                <strong>Operation strength:</strong> 76.2% accuracy on subtraction
             </div>
         </div>
         
         <div class="model-card">
             <div class="model-info">
-                <span class="model-name">aquiffoo/aquif-r1-1b</span>
-                <span class="model-params">1.14B parameters + CoT reasoning</span>
+                <span class="model-name">BrainrotLM-Assistant-362M</span>
+                <span class="model-params">362M parameters</span>
+            </div>
+            <p>Shows moderate mathematical abilities with 12.0% overall accuracy. Demonstrates particular strength in division operations (38.9%) and subtraction (22.7%).</p>
+            <div class="performance-highlight">
+                <strong>Operation strength:</strong> 38.9% accuracy on division
             </div>
-            <p>Despite being significantly larger and incorporating chain-of-thought reasoning capabilities, this model shows no measurable performance on the tested mathematical problems.</p>
         </div>
 
         <h2>Performance Analysis</h2>
@@ -215,11 +218,11 @@
         <div class="chart-container">
             <div class="chart">
                 <img src="6818abac-ba0b-4fae-aaf4-d42a9d4ebc04.png" alt="Chart showing model accuracy by operation type">
-                <div class="chart-caption">Figure 1: Accuracy by Operation Type (%)</div>
+                <div class="chart-caption">Figure 1: Accuracy by Mathematical Operation (%)</div>
             </div>
             <div class="chart">
                 <img src="284d12f0-c0f1-4e2f-8455-1ad7fefc3e1e.png" alt="Chart showing model performance on math problems">
-                <div class="chart-caption">Figure 2: Correct vs Incorrect Answers (1000 questions each)</div>
+                <div class="chart-caption">Figure 2: Correct vs Incorrect Answers (100 questions each)</div>
             </div>
         </div>
 
@@ -246,24 +249,60 @@
             <tbody>
                 <tr class="highlight">
                     <td>MathGPT2 (81.9M)</td>
-                    <td>63.1%</td>
-                    <td>59.3%</td>
-                    <td>34.9%</td>
-                    <td>22.3%</td>
-                    <td>8.8%</td>
-                    <td>42.7%</td>
+                    <td>58.3%</td>
+                    <td>57.1%</td>
+                    <td>45.0%</td>
+                    <td>24.1%</td>
+                    <td>0.0%</td>
+                    <td>42.0%</td>
+                </tr>
+                <tr>
+                    <td>aquif-moe-800m (800M)</td>
+                    <td>54.5%</td>
+                    <td>76.2%</td>
+                    <td>21.9%</td>
+                    <td>18.2%</td>
+                    <td>0.0%</td>
+                    <td>39.0%</td>
+                </tr>
+                <tr>
+                    <td>BrainrotLM-Assistant-362M (362M)</td>
+                    <td>0.0%</td>
+                    <td>22.7%</td>
+                    <td>0.0%</td>
+                    <td>38.9%</td>
+                    <td>0.0%</td>
+                    <td>12.0%</td>
+                </tr>
+                <tr>
+                    <td>gonzalez-v1</td>
+                    <td>5.3%</td>
+                    <td>8.3%</td>
+                    <td>0.0%</td>
+                    <td>0.0%</td>
+                    <td>0.0%</td>
+                    <td>3.0%</td>
                 </tr>
                 <tr>
                     <td>VLM-1 (124M)</td>
-                    <td>2.6%</td>
-                    <td>3.1%</td>
+                    <td>3.4%</td>
+                    <td>0.0%</td>
+                    <td>0.0%</td>
+                    <td>4.3%</td>
+                    <td>0.0%</td>
+                    <td>2.0%</td>
+                </tr>
+                <tr>
+                    <td>gpt2</td>
+                    <td>0.0%</td>
+                    <td>7.4%</td>
+                    <td>0.0%</td>
                     <td>0.0%</td>
-                    <td>14.2%</td>
                     <td>0.0%</td>
-                    <td>4.8%</td>
+                    <td>2.0%</td>
                 </tr>
                 <tr>
-                    <td>aquif-r1-1b (1.14B)</td>
+                    <td>Snowflake-G0-Release</td>
                     <td>0.0%</td>
                     <td>0.0%</td>
                     <td>0.0%</td>
@@ -271,17 +310,16 @@
                     <td>0.0%</td>
                     <td>0.0%</td>
                 </tr>
-                
             </tbody>
         </table>
 
         <h2>Key Observations</h2>
         <ul>
-            <li><strong>Size doesn't always matter:</strong> MathGPT2 with only 81.9M parameters demonstrates impressive mathematical abilities, outperforming larger models.</li>
-            <li><strong>Operation specialization:</strong> MathGPT2 excels at addition (63.1%) and subtraction (59.3%), while VLM-1 shows particular strength in division operations (14.2%).</li>
+            <li><strong>Size doesn't always matter:</strong> MathGPT2 with only 81.9M parameters demonstrates impressive mathematical abilities, achieving 42.0% overall accuracy.</li>
+            <li><strong>Operation specialization:</strong> MathGPT2 excels at addition (58.3%) and subtraction (57.1%), while aquif-moe-800m shows exceptional strength in subtraction operations (76.2%).</li>
             <li><strong>Architectural importance:</strong> The results suggest that architecture design and training approach may be more important than raw parameter count for specialized tasks.</li>
-            <li><strong>Zero performance:</strong> Four of the tested models showed no measurable mathematical ability on this test set.</li>
-            <li><strong>Chain-of-thought limitations:</strong> Despite having CoT capabilities, aquif-r1-1b did not demonstrate mathematical reasoning abilities in this benchmark.</li>
+            <li><strong>Zero performance:</strong> One of the tested models (Snowflake-G0-Release) showed no measurable mathematical ability on this test set.</li>
+            <li><strong>Division specialists:</strong> BrainrotLM-Assistant-362M shows specific strength in division operations (38.9%) despite lower performance in other areas.</li>
         </ul>
 
         <div class="key-finding">
@@ -290,14 +328,14 @@
         </div>
 
         <h2>Conclusion</h2>
-        <p>This analysis demonstrates that extremely small language models can exhibit significant mathematical reasoning abilities, with models as small as 81.9M parameters showing the ability to solve basic arithmetic problems. The standout performer, MathGPT2 with only 81.9M parameters, achieved an impressive 42.7% accuracy on a diverse set of 1000 mathematical questions.</p>
+        <p>This analysis demonstrates that extremely small language models can exhibit significant mathematical reasoning abilities, with models as small as 81.9M parameters showing the ability to solve basic arithmetic problems. The standout performer, MathGPT2 with only 81.9M parameters, achieved an impressive 42.0% accuracy on a diverse set of 100 mathematical questions.</p>
         
         <p>These findings suggest that efficient architectural design and specialized training approaches may be more important than raw parameter count when optimizing for specific reasoning capabilities. This could have significant implications for resource-constrained applications where deploying massive models is impractical.</p>
 
         <p>Future research directions could include investigating what specific architectural choices enable these compact models to perform mathematical operations, and how these insights might be applied to develop more efficient specialized models for other reasoning tasks.</p>
 
         <div class="footer">
-            <p>Data analysis based on benchmark results for MathGPT2 (81.9M), VLM-1 (124M), aquif-r1-1b (1.14B), and other models</p>
+            <p>Data analysis based on benchmark results for MathGPT2 (81.9M), aquif-moe-800m (800M), BrainrotLM-Assistant-362M (362M), and other models</p>
             <p>© 2025 • Created for educational purposes</p>
         </div>
     </div>